Antibiotikaeinsatz in der Bayerischen Schweinehaltungspraxis ABYS: Antibiotikaeinsatz und Antibiotikaresistenz in ökologischen Betrieben

Between 2012 and 2014, ABYS study recorded antibiotic use, detection and resistance data for 23 organic and 35 conventional pig farms. Antibiotic contents of farm-made fertilizers were assessed by LC/MS-MS. Phenotypic antimicrobial resistance was investigated in Escherichia (E.) coli (indicator bacteria); antimicrobial resistance genes of the total bacterial microbiota (sul(II), tet(A), tet(B), tet(M); marker Measured in nUDD (number of animals treated multiplied by treatment days), colistin was the most frequently used antibiotic, in organic farms followed by tylosin, doxycycline and amoxicillin. Antibiotic residues were rarely detected; however, manure contained up to 10^8 antimicrobial resistance genes per gram; concentrations were higher when the antibiotic had been used on farm. In six farms, antimicrobial resistant E. coli were tracked from the moment when pigs were placed on farm. Some isolates carried a broad variety of resistances from the very beginning that were maintained until slaughter, despite the fact that partly no antibiotics were applied during fattening. Approaches for reducing carry-over of antimicrobial resistant bacteria will be discusse

The immunopathology of canine vector-borne diseases

The canine vector-borne infectious diseases (CVBDs) are an emerging problem in veterinary medicine and the zoonotic potential of many of these agents is a significant consideration for human health. The successful diagnosis, treatment and prevention of these infections is dependent upon firm understanding of the underlying immunopathology of the diseases in which there are unique tripartite interactions between the microorganism, the vector and the host immune system. Although significant advances have been made in the areas of molecular speciation and the epidemiology of these infections and their vectors, basic knowledge of the pathology and immunology of the diseases has lagged behind. This review summarizes recent studies of the pathology and host immune response in the major CVBDs (leishmaniosis, babesiosis, ehrlichiosis, hepatozoonosis, anaplasmosis, bartonellosis and borreliosis). The ultimate application of such immunological investigation is the development of effective vaccines. The current commercially available vaccines for canine leishmaniosis, babesiosis and borreliosis are reviewed

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation